Fix unit test.

python/paddle/fluid/contrib/tests/test_quantize_transpiler.py

@@ -208,17 +208,21 @@ class TestQuantizeTranspiler(unittest.TestCase):
             paddle.dataset.mnist.test(), batch_size=batch_size)
         feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
 
+        with fluid.program_guard(main):
             for _ in range(iter):
                 data = train_reader().next()
                 loss_v = exe.run(program=main,
                                  feed=feeder.feed(data),
                                  fetch_list=[loss])
-        test_data = test_reader().next()
 
+        with fluid.program_guard(test_program):
+            test_data = test_reader().next()
             f_var = fluid.framework.get_var('conv2d_1.tmp_0', test_program)
-        w_var = fluid.framework.get_var('conv2d_1.w_0.quantized', test_program)
+            w_var = fluid.framework.get_var('conv2d_1.w_0.quantized',
+                                            test_program)
             # Testing during training
-        test_loss1, f_v1, w_quant = exe.run(program=test_program,
+            test_loss1, f_v1, w_quant = exe.run(
+                program=test_program,
                 feed=feeder.feed(test_data),
                 fetch_list=[loss, f_var, w_var])
 
@@ -229,8 +233,8 @@ class TestQuantizeTranspiler(unittest.TestCase):
             test_loss2, f_v2 = exe.run(program=test_program,
                                        feed=feeder.feed(test_data),
                                        fetch_list=[loss, fv2])
-        self.assertAlmostEqual(test_loss1, test_loss2, delta=1e-5)
-        self.assertAlmostEqual(f_v1.all(), f_v2.all(), delta=1e-5)
+            self.assertAlmostEqual(test_loss1, test_loss2, delta=1e-3)
+            self.assertTrue(np.allclose(f_v1, f_v2, rtol=1e-05, atol=1e-05))
             w_freeze = np.array(fluid.global_scope().find_var('conv2d_1.w_0')
                                 .get_tensor())
             self.assertEqual(np.sum(w_freeze), np.sum(w_quant))
@@ -238,10 +242,11 @@ class TestQuantizeTranspiler(unittest.TestCase):
             # Convert parameter to 8-bit.
             quant_transpiler.convert_to_int8(test_program, place)
             # Save the 8-bit parameter and model file.
-        fluid.io.save_inference_model('model_8bit', ['image', 'label'], [loss],
-                                      exe, test_program)
+            fluid.io.save_inference_model('model_8bit', ['image', 'label'],
+                                          [loss], exe, test_program)
             # Test whether the 8-bit parameter and model file can be loaded successfully.
-        [infer, feed, fetch] = fluid.io.load_inference_model('model_8bit', exe)
+            [infer, feed, fetch] = fluid.io.load_inference_model('model_8bit',
+                                                                 exe)
             # Check the loaded 8-bit weight.
             w_8bit = np.array(fluid.global_scope().find_var('conv2d_1.w_0.int8')
                               .get_tensor())
@@ -251,9 +256,11 @@ class TestQuantizeTranspiler(unittest.TestCase):
 
     def test_freeze_program_cuda(self):
         if fluid.core.is_compiled_with_cuda():
+            with fluid.unique_name.guard():
                 self.freeze_program(True)
 
     def test_freeze_program_cpu(self):
+        with fluid.unique_name.guard():
             self.freeze_program(False)